In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling. The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field. Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference. Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models. Part III includes discussion of missing data and robust methods, assessment of fit and software. Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines. Combining practical pieces with overviews of the field, this Handbook is essential reading for any student or researcher looking to apply multilevel techniques in their own research.
Jeffrey S. Simonoff is Professor of Statistics at the NYU Stern School of Business. He is a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute. He is author or coauthor of roughly 100 articles and five books on the theory and applications of statistics. Brian D. Marx is a Professor of Statistics at Louisiana State University. His main research interests include smoothing, ill-conditioned regression problems, high-dimensional chemometric applications; and he has numerous publications on these topics. He is past president of the Statistical Modelling Society, and is currently member of the Executive Committee of this same international professional society. He is coauthor of the book Regression: Models, Methods, and Applications, as well as, the co-editor of the Sage Handbook on Multilevel Modelling.
Notes on Contributors Preface Multilevel Modeling - Jeffrey S Simonoff, Marc A Scott and Brian D Marx PART ONE: MULTILEVEL MODEL SPECIFICATION AND INFERENCE The Multilevel Model Framework - Jeff Gill and Andrew Womack Multilevel Model Notation - Establishing the Commonalities - Marc A Scott, Patrick E Shrout and Sharon L Weinberg Likelihood Estimation in Multilevel Models - Harvey Goldstein Bayesian Multilevel Models - Ludwig Fahrmeir, Thomas Kneib, and Stefan Lang The Choice between Fixed and Random Effects - Zac Townsend,Jack Buckley, Masataka Harada and Marc A Scott Centering Predictors and Contextual Effects - Craig K Enders Model Selection for Multilevel Models - Russell Steele Generalized Linear Mixed Models - Overview - Geert Verbeke and Geert Molenberghs Longitudinal Data Modeling - Nan M Laird and Garrett M Fitzmaurice Complexities in Error Structures Within Individuals - Vicente Nunez-Anton and Dale L Zimmerman Design Considerations in Multilevel Studies - Gerard van Breukelen and Mirjam Moerbeek Multilevel Models and Causal Inference - Jennifer Hill PART TWO: VARIATIONS AND EXTENSIONS OF THE MULTILEVEL MODEL Multilevel Functional Data Analysis - Ciprian M Crainiceanu, Brian S Caffo and Jeffrey S Morris Nonlinear Models - Lang Wu and Wei Liu Generalized Linear Mixed Models: Estimation and Inference - Charles E McCulloch and John M Neuhaus Categorical Response Data - Jeroen Vermunt Smoothing and Semiparametric Models - Jin-Ting Zhang Penalized Splines and Multilevel Models - Goeran Kauermann and Torben Kuhlenkasper Hierarchical Dynamic Models - Marina Silva Paez and Dani Gamerman Mixture and Latent Class Models in Longitudinal and Other Settings - Ryan P Browne and Paul D McNicholas Multivariate Response Data - Helena Geys and Christel Faes PART THREE: PRACTICAL CONSIDERATIONS IN MODEL FIT AND SPECIFICATION Robust Methods for Multilevel Analysis - Joop Hox and Rens van de Schoot Missing Data - Geert Molenberghs and Geert Verbeke Lack of Fit, Graphics, and Multilevel Model Diagnostics - Gerda Claeskens Multilevel Models: Is GEE a Robust Alternative in the Presence of Binary Endogenous Regressors? - Robert Crouchley Software for Fitting Multilevel Models - Andrzej T Galecki and Brady T West PART FOUR: SELECTED APPLICATIONS Meta-Analysis - Larry V Hedges and Kimberly S Maier Modeling Policy Adoption and Impact with Multilevel Methods - James E Monogan III Multilevel Models in the Social and Behavioral Sciences - David Rindskopf Survival Analysis and the Frailty Model: The effect of education on survival and disability for older men in England and Wales - Ardo van den Hout and Brian D M Tom Point-Referenced Spatial Modeling - Andrew O Finley and Sudipto Banerjee Market Research and Preference Data - Adam Sagan Multilevel Modeling for Scoial Networks and Relational Data - Marijtje A J Van Duijn Name Index Subject Index
...This handbook attempts to cover an intricate and multifaceted topic and delivers what is expected of it very successfully and at good cost considering its all-purpose usefulness. This handbook is a must-have consultation book for researchers interested in multilevel modelling from a wide variety of disciplines, if not all. -- Patricio Troncosco, Cathie Marsh Institute for Social Research, The University of Manchester
